simon haykin neural network ebook download
NN is one way of simulating the way humans learn things.
It is a network of nodes (Neurons, Nerves), relating with each other with links (synapses). Each link has a Wait. So we can simulate it with a matrix, each row representing the waits between a certain node and the other nodes. This network can have layers, it means there are two or more sets of nodes, each have a matrix of its own, and there are some links between layers. The first set (which may be the only set) has some links to the outside, representing INPUTS. The final set (which can be the only set, too) has outer links as the OUTPUT of the network. You apply the inputs to the INPUT, and you get the output from OUTPUT. Learning, means changing, in some manner, the waits of the nodes, so that the networks give the right outputs with certain inputs. So, they can give the outputs of unknown inputs, by learning the relation between the INPUTS and OUTPUTS of your certain problem.
There are some different kind of Nets, such as RBF (Radial Basis Functions), Multi-Layer perceptrons (MLP), Kohonen Networks, so on... .
You can find a comprehensive description of NNs here:
http://www.cs.stir.ac.uk/~lss/NNIntro/InvSlides.html
.matt